fksainetwork

A neural network capable of learning and calculating output.
This project is just made for fun :)

Please go to Inner Workings to read more.

Usage

[dependencies]
fksainetwork = "0.2.0"

Example (Feed Forward)

let mut network = Network::new(2, &[ //2 neuron inputs
            (10, Initialization::He, Activation::Sigmoid, false), //10 hidden neurons
            (2, Initialization::Xavier, Activation::LeakyReLU, true) //2 neuron outputs, true: batch normalisation
        ], Loss::BinaryCrossEntropy, true);
//or: let network = load_network("path/to/network-file");

//calculating
let input = vec![1.0, 1.0];
let output = network.calculate(&input); //calculate
println!("{:?}", output);

//learning
//batch size of 2
network.learn(0.01,
  &vec![vec![0.0, 1.0], vec![0.0, 3.0]], //input values, batch size of 2
  &vec![vec![1.0, 0.0], vec![0.0, 1.0]] //expected values
);

//NOTE: if you call 'learn', u do not need to call 'calculate' beforehand

//save
save_network("path/network-file", &network);

Example (Convolutional)

let network = ConvolutionalNetwork::new(
            //convolution layers
            &[
                (2, &[Initialization::Xavier;20], Activation::ReLU, 2, Pooling::Max), //20 channels, kernel 2x2, pooling max 2.0
                (3, &[Initialization::Xavier;40], Activation::ReLU, 2, Pooling::Max) //40 channels, kernel 3x3, pooling max 2.0
            ],
            13, 13, 1, //input size of w: 13, h: 13, channels: 1
            //input similar to the Feed Forward Network
            &[
                (3, Initialization::Xavier, Activation::LeakyReLU, false)
            ],
            Loss::BinaryCrossEntropy, true
        );
//or: let network = load_cnn_network("path/to/network-file");

//pretend these samples are actual images of something
let sample0 = Matrix::new(13, 13);
let sample1 = Matrix::new(13, 13);
let sample2 = Matrix::new(13, 13);

//calculate
let output = network.calculate(&vec![&sample0, &sample1, &sample2]);
println!("{:?}", output);

//learn
network.learn(0.04, &vec![&sample0, &sample1, &sample2], &vec![[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]);

//print outputs
cnn_network_bmp("path/to/directory", &network);

//save network
save_cnn_network("path/to/network-file", &network);

Update Patches :)

0.1.3: Added Tanh Activation Function.
0.1.3: Improved Docs.
0.2.0: The save_network and load_network functions now use str instead of String for the path.
0.2.0: Improved learning algorithm.
0.2.0: Added the Convolutional Neural Network.
0.2.0: Added more Loss functions and Initialization functions.
0.2.0: Changed learn_bpg_mse to learn as the loss function is now a function parameter.
0.2.0: Changes to the feed forward network "new()" function parameter.